Verfahren zur Bestimmung der Einbruchswahrscheinlichkeit des Verkehrsablaufs auf Autobahnen und Anwendung in der Verkehrssteuerung

Capacity is one of the relevant values to describe traffic flow on freeway segments. It depends on various conditions such as roadway, traffic, environment and control conditions. The magnitudes of the influencing values on capacity, that are described in guidelines, are mostly based on just a few research studies with little spatial and temporal data sources. In the past, capacity was defined as a deterministic value for given base conditions. Even in the currently valid guidelines this definition is used. Newer research studies, however, prove that capacity has to be interpreted as a stochastically distributed value. The more the traffic flow increases the higher the risk of traffic breakdowns. Aim of the current study is to derive general statements on how capacity is influenced by various conditions based on a probabilistic description of capacity. The model developed for this purpose shall be applicable universally without any sophisticated calibration. The found conclusions to describe capacity as a probabilistic value shall be statistically verified and well processed so that it can be used for various applications of traffic engineering and traffic control. Therefore, an algorithm is developed which is able to anticipate traffic breakdowns and this way allows a preventive control of the traffic flow. For various combinations of different influencing factors (wet surface, darkness, type of day, number of lanes, grades, location in urban/rural areas) several regression curves were determined. At ideal conditions it showed that for grade terrain there was a significant decrease of the traffic flow, which led to a breakdown probability of 50%, of 13.4%. Also, for negative grade a reduction of 4.0% could be detected. For two lane facilities in urban areas it was found that the flow that causes a breakdown in 50% of all cases is 6.3% higher than in rural areas. In a comparison between three and two lane facilities in urban areas it turned out, that the capacity per lane is significantly higher on two lane facilities. The temporary factors have a great influence on the probability of breakdown. For wet surface conditions there is a significant decrease of 12.9% of the flow rate which causes a breakdown of 50%. During darkness there is a reduction of 14.3%. If the traffic flow mainly consists of recreational traffic, it can be stated that the flow rate at a breakdown risk of 50% is 4.7% lower than during commuter traffic. As the percentage of heavy vehicles increases the breakdown risk at a given flow rate increases, too. In addition, the influence of a corridor management application on the breakdown probability was analysed by means of two detectors on the A12 in the area of Innsbruck. It was observed that the run of the curve is changed at medium and high flow rates. Therefore, it can be stated, that here the traffic flow is more stable compared to sections without dynamic traffic control. The deployment of a corridor management application increases the flow rate, which leads to a probability of breakdown of 50%, up to 3.5%. The findings regarding the breakdown probability can be used in traffic engineering applications. To do so, the algorithm AIX-ProB (Anticipation of Incidents with extended Probability of Breakdown) was developed. This algorithm, which is based on the breakdown probability theory, considers temporary influencing factors. Another important goal when developing the AIX-ProB was to achieve an applicable implementation without any complex parameters of configuration so that the algorithm can be used for any location. AIX-ProB was tested under open loop conditions for the use in corridor management applications. In comparison to other common control algorithms with a preventive character, it can be stated that AIX-ProB shows a good ratio between detection and false alarm rate. The algorithm is reliably able to anticipate traffic situations and thus trigger a specific measure to counter this situation. Especially the consideration of temporary influencing factors on the breakdown probability has a very positive effect on the quality of the algorithm. All in all, it could be verified that the use of AIX-ProB is suitable for traffic control using corridor management applications. This thesis made it possible to confirm the assumption that capacity is a probabilistic value that can be described using the probability of breakdown. The breakdown probability is dependent on various influencing factors the dimensions of which could be determined on the basis of an analysis of a large amount of base data. Furthermore, it could be verified, that breakdown probability can be applied in traffic quality evaluation and for traffic control